Name Mode Size
R 040000
data 040000
man 040000
vignettes 040000
DESCRIPTION 100644 2 kb
NAMESPACE 100644 1 kb 100644 3 kb
demo_RCSL.R 100644 2 kb
# Rank Constrained Similarity Learning (RCSL) RCSL is an R toolkit for single-cell clustering and trajectory analysis using single-cell RNA-seq data. ## Installation This package can be insatlled through devtools in R: ```{r} $ R > library("devtools") > devtools::install_github("QinglinMei/RCSL",build_vignettes = T) ``` Now RCSL can be loaded in R: ```{r} > library(RCSL) ``` ## Input The input to RCSL is a normalized data matrix with columns being cells and rows being genes in log(CPM+1), log(RPKM+1), log(TPM+1) or log(FPKM+1) format; or a data file in RDS format. ## Usage We provide an example script to run RCSL in *demo_RCSL.R*. The nine functions of RCSL can also be run independently. Function | Description -----------|---------- `GenesFilter` | Perform genes filtering. `SimS` | Calculate the initial similarity matrix S. `NeigRepresent` | Calculate the neighbor representation of cells. `EstClusters` | Estimate the optimal number of clusters C. `BDSM` | Learn the block-diognal matrix B. `PlotMST` | Construct MST based on clustering results from RCSL. `PlotPseudoTime` | Infer the pseudo-temporal ordering of cells. `getLineage` | Infer the lineage based on the clustering results and the starting cell. `PlotTrajectory` | Plot the developmental trajectory based on the clustering results and the starting cell. ## Example: Load packages: ```{r} > library(RCSL) > library(SingleCellExperiment) > library(ggplot2) > library(igraph) ``` Load Goolam dataset: ```{r} > origData <- readRDS("./Data/Goolam.rds") > data <- logcounts(origData) > label <- origData$cell_type1 > DataName <- "Goolam" ``` Generating clustering result: ```{r} > res_RCSL <- RCSL(data) ``` Calculating Adjusted Rand Index: ```{r} > ARI_RCSL <- igraph::compare(res_RCSL$y, label, method = "adjusted.rand") ``` Trajectory analysis: ```{r} > label <- origData$cell_type1 > res_TrajecAnalysis <- TrajectoryAnalysis(res_RCSL$gfData, res_RCSL$drData, res_RCSL$S, clustRes = res_RCSL$y, TrueLabel = label, startPoint = 1, dataName = DataName) ``` Display the plot of constructed MST: ```{r} > res_TrajecAnalysis$MSTPlot ``` Display the plot of the pseudo-temporal ordering ```{r} > res_TrajecAnalysis$PseudoTimePlot ``` Display the plot of the inferred developmental trajectory ```{r} > res_TrajecAnalysis$TrajectoryPlot ``` A vignette in R Notebook format is available [here]( ## Required annotations for RCSL 1) The RCSL package requires three extra packages: namely the *SingleCellExperiment* package (see to read the *SingleCellExperiment* object, the *igraph* package (see to find the stronggest connected components and the *ggplot2* package (see to plot the developmental trajectory and MST. 2) The dataset for the demonstration purpose in the directory *Data* was from This dataset is stored in both RDS and text formats. ## DEBUG Please feel free to contact us if you have problems running our tool at